SUMMARY:

• Experienced in system design, data architecture and modeling - relational for OLTP systems and dimensional for data warehouses (Inmon methodology) and data marts (Kimball methodology), data governance, master data and metadata management, operational and BI reporting solutions for Fortune 500 IT, banking, insurance and federal clients.
• Involved in implementing Big Data technologies including Hadoop ecosystem, Enterprise Data Lakes, MPP Databases, and NoSQL storage architecture for semi-structured, and unstructured data.
• Experienced in designing migration and integration solutions for data from disparate sources.
• Developed proprietary methods for metadata management, Data Quality Assurance/Quality Control, Governance policies for federal clients.
• Experienced in enhancing complex enterprise data models, accurately representing the logical and DB nuances in physical data models.
• Designed data models (ER and dimensional) for database platforms such as Oracle, SQL Server, and DB2.
• Experienced in metadata preparation using modeling and DB tools and validation with respective stakeholders.
• Experienced in documentation and conducting walkthroughs on data lineage, system specific data flows, UML Diagrams, conceptual and logical data models for project stakeholders at various levels in the organization.
• Professionally trained in concepts of TOGAF and Zachman enterprise architecture frameworks, application development, object-oriented modeling and graph data modeling and data management approaches to Big Data, NoSQL databases, columnar databases, AWS and data lakes and data mining techniques.
• Experienced in business process modeling.
• Exceptional ability to learn and master in new technologies and work under tight timelines.

TECHNICAL SKILLS:

Data domains: Supply chain & logistics, Finance (expense management, fraud and disputes), Insurance (Auto, Property, Health, Life), Geospatial information.

Data Modeling Concepts and tools: CA Erwin, ER/Studio, Infosphere Data Architect, XML, UML, Data Vault modeling, Kimball and Inmon approaches

Architecture concepts and tools: TOGAF 9.0 and Zachman

Sparx Enterprise Architect, AWS.

Analysis and Reporting tools: Tableau, SAS, Business Objects, SSRS

Database platforms and tools: Oracle, SQL Server, DB2, Greenplum, Teradata, NoSQL, Hadoop,Sybase, AWS

Data Management tools: Informatica Data Quality, Informatica MDM, ASG Rochade, DAG Metacenter

ETL/ELT tools: Informatica PowerCenter, ODI, SSIS, Talend

Project Management tools: MS Team Foundation Server, MS Project

SQL tools: SQL Developer, WinSQL, SQL Server Management Studio, pgAdmin, DBeaver, TOAD

Programming: Core Java, Python, MATLAB

GIS tools: ESRI ArcGIS, LP 360

Process Modeling tools: Visio, ARIS, Bizagi

PROFESSIONAL EXPERIENCE:

Confidential

Sr. Data Modeler

Responsibilities:

• Collaborate with SMEs, enterprise architects and developers to identify changes made in specific subject areas.
• Build source to target mapping specs based on data structures obtained in various formats such as XML (XSD files), SAP proprietary document formats.
• Leverage SAP built-in functionalities to extract metadata from SAP GUI such as relationships, descriptions, sample values, etc., to build draft logical models.
• Conduct peer-reviews and client-validation of the model to ensure the naming and metadata are appropriate.
• Deliver data model in appropriate formats such as XSD, WSDL etc., to be consumed by development teams to physicalize the schema.
• Develop modeling tool macros to semi-automate to handle modeling activities such as metadata imports and exports, model consistency checks etc.
• Develop mapping documents between enterprise logical data model and the dimensional models.
• EDW data migration
• Identify Common KPIs and metrics across data domains to be migrated to data lake to support business reporting and analytics.
• Identify the core fact and dimension tables to build data marts for applicable subject areas in AWS.
• Build current state mappings from Source system (SAP) to staging to Data Vault to EDW.
• Build future state source to target mappings (sources in data lake)and derivation logic to provision reporting directly from data lake to decouple current EDW and reporting.
• Identify and in corporate ETL rules required to cleanse, harmonize and transform data such as current row logic from raw layer to serving layer in the data lake.
• Enhance and enrich existing dimensional models to synchronize upstream SAP application and downstream reporting environments based out of AWS through aggregated fact tables.

Environment: SAP, SQL Server 2016,Amazon Redshift, Aginity Workbench for Redshift, Amazon EMR, AWS Athena, Qlik View

Confidential

Data Modeler

Responsibilities:

• Collaborated with SMEs to identify and gather metadata for critical data elements for subject areas such as Person, Merchant, Expense and Account.
• Reverse-engineered legacy data structures into physical and logical Entity-Relationship models and discovered business rules.
• Mapped data elements using their semantics from legacy systems to the enterprise data model.
• Delineated and conducted walkthroughs of process flows using UML diagrams to identify data integration points to be secured.
• Enriched and validated the as-is logical metadata such as descriptions, allowed values etc., with appropriate business stakeholders.
• Resolved structural inconsistencies of critical data elements across multiple source systems running on different databases and involved in data security implementation for sensitive data elements.
• Involved in ontology development to address inconsistencies in semantics among business domains in a globally distributed database environment.
• Assessed and quantified system impacts of database alteration such as schema changes, stored procedure changes etc., in accordance with future state system architecture.
• Developed source to target mapping documents between existing databases and cloud based NoSQL databases such as AWS to support data extracts shared with client partners.
• Investigated and recommend appropriate practices in data security such as tokenization, encryption, Multi-Factor Authentication etc.
• Identified ETL logic for sensitive data elements to be partially reused during initial data encryption efforts.
• Involved in database changes such as data conversion, updating stored procedures, SQL packages, column data type changes etc., to implement data encryption.

Confidential

Data Modeler/Architect

Responsibilities:

• Derived data requirements and supported solution design and development estimation efforts.
• Profiled source systems using SQL queries and Informatica Data Quality to determine data types, data lengths and relationships.
• Determined data security and compliance requirements; data archival and purge policies, data quality and integrity constraints etc.
• Collaborated with architects to review candidate architecture for projects in Enterprise Architect.
• Assessed system impacts due to data migration from legacy databases to enterprise data warehouse.
• Involved in architecting data migration and delivery solutions surrounding Hadoop implementation.
• Developed and/or Implement data matching process for attributes to be mastered in Informatica MDM.
• Identified, validated and leveraged sources and target database schemas, ensuring conformity and reusability.
• Involved in data lineage and Informatica ETL source to target mapping development, complying with data quality and governance standards.
• Created and enhanced future-friendly logical data models and conducted design walkthroughs with internal teams and end users.
• Generated physical data models and DDL scripts for database objects, incorporating enterprise standards and industry best practices for the target database.
• Created and administered database objects such as tables, sequences, history triggers, stored procedures etc., in sandbox environments for preliminary development and testing.
• Supported database implementations, performance tuning such as query execution plan, data distribution and partitioning, issue resolution and clean-up efforts.
• Converted data models from Erwin to ER/Studio version, accounting for relevant metadata such as attribute domains, naming standard templates, annotations, cardinality etc.
• Researched and involved in developing data management and modeling standards for platforms that support unstructured data such as Hadoop.
• Coordinated with offshore ETL development and testing and reporting teams.
• Documented and tracked data model changes involved in production defect fixes to retrofit database structures across development environments.
• Validated that semantics of data elements being reported align with business requirements.
• Captured, validated and published metadata in accordance with enterprise data governance policies and MDM taxonomies.
• Performed data model repository check-in and enterprise metadata releases to the corporate portal, MetaCenter.
• Identified confidential and PII data elements and involve in enforcing appropriate protective measures such as tokenizing, masking, redacting etc. for data in flight and at rest.
• Developed Visual Basic based ER/Studio modeling tool macros to improve productivity in customizing off-the-shelf Guidewire data models and dictionaries for components such as Policy Center, Claim Center, Billing Center, Contact Manager etc.
• Harmonize Gosu/Guidewire modeling conventions with enterprise data standards and best practices
• Reviewed changes for object reuse, standards, naming, data type, descriptional metadata, enforcement of relationships based on rationale (edge foreign key, one-to-one), subtyping etc.
• Synced reverse-engineered data model with periodic deployments (new objects and changes to existing objects)
• Supported ETL mapping between GW transactional database - ODS - DW. Identifying right Guidewire backend table sources and downstream target tables (Approx. table counts: PLPC 1500, CM 300+, CLPC 1000+ etc.)

Confidential

Data Analyst/Modeler

Responsibilities:

• Researched academic and industry literature; evaluated and reported the best processes for QA/QC of spatial data.
• Developed source-specific data quality metrics for GIS data acquisition initiatives from data vendors.
• Developed data requirement matrix surrounding data quality levels for cost-benefit analysis.
• Profiled data using GIS tools as well as SQL for quality assessment and reported on data quality dimensions such as conformity across multiple data subsets, completeness, accuracy etc.
• Involved in developing a proprietary data matching and duplicate record clean-up process using GIS tools, MATLAB programming.
• Cleansed, standardized and integrated data across multiple sources to achieve a single source of truth.
• Involved in developing an in-house MDM capability using ArcGIS and Oracle database.
• Involved in database and ETL mapping enhancements to cross-reference and integrate multi-source datasets under an experimental multi-granular data integration project.
• Supported database tuning to optimize ArcGIS web service data delivery performance.
• Evaluated and determined the best techniques to derive spatial data products such as digital elevation models, break lines etc., for mapping purposes.
• Developed proprietary data quality control functionality into ArcGIS Python models and plugin scripts to ensure the quality of produced GIS derivatives.

Confidential

Data Analyst/Developer

Responsibilities:

• Documented processes for different source systems/core banking products such as Mutual funds, Sanctions, Transaction monitoring, Wires, Loans, Deposits, ACH, Custody etc.
• Identified, validated and documented critical data elements and their definitions to be published in Corporate glossary.
• Assisted MDM architects and solution architects in discovering the relationships among different data domains and data flows into the MDM system.
• Identified and implemented missing business rules in the database using check constraints & triggers to verify transaction commit rules in coordination with DBAs.
• Involved in data migration from legacy data management system to Agile PLM system.
• Built and deployed SSIS packages in Development and Production environments to import and export data to/from spreadsheets, SQL Server tables and flat files.
• Configured daily batch loads (Full & Incremental) into Staging and ODS areas, troubleshooting issues and handling events and errors using SSIS.
• Written SQL queries for data retrieval and testing in development databases prior to migration.
• Used various transformations in SSIS Dataflow, Control Flow using for loop Containers and Fuzzy Lookups etc.
• Developed reports such as Parameterized reports, ad-hoc reports, Drill Down, Drill through, Cross tab reports in SSRS.